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xls-r-1b-cv_8-fr
Plim/xls-r-1b-cv_8-fr
xls-r-1b-cv_8-fr is a automatic speech recognition model from Plim. Use it when you need speech turned into text. It is set up for transformers. The card lists the license as apache-2.0.
This model is a fine-tuned version of facebook/wav2vec2-xls-r-1b on the MOZILLA-FOUNDATION/COMMONVOICE80 - FR dataset.
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At a glance
- Task
- Automatic Speech Recognition
- Library
- transformers
- License
- apache-2.0
- Model type
- wav2vec2
- Access
- Public
- Created
- Mar 2, 2022
- Updated
- Mar 24, 2022
- SHA
- 14d95dbc
Model card
From the Hugging Face model README
Model description
This model is a fine-tuned version of facebook/wav2vec2-xls-r-1b on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - FR dataset.
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 7.5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 6.0
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.9827 | 0.29 | 1000 | inf | 0.2937 |
| 1.0203 | 0.57 | 2000 | inf | 0.2711 |
| 1.0048 | 0.86 | 3000 | inf | 0.2620 |
| 0.9858 | 1.15 | 4000 | inf | 0.2522 |
| 0.9709 | 1.43 | 5000 | inf | 0.2365 |
| 0.9347 | 1.72 | 6000 | inf | 0.2332 |
| 0.9256 | 2.01 | 7000 | inf | 0.2261 |
| 0.8936 | 2.29 | 8000 | inf | 0.2203 |
| 0.877 | 2.58 | 9000 | inf | 0.2096 |
| 0.8393 | 2.87 | 10000 | inf | 0.2017 |
| 0.8156 | 3.15 | 11000 | inf | 0.1936 |
| 0.8015 | 3.44 | 12000 | inf | 0.1880 |
| 0.774 | 3.73 | 13000 | inf | 0.1834 |
| 0.8372 | 4.01 | 14000 | inf | 0.1934 |
| 0.8075 | 4.3 | 15000 | inf | 0.1923 |
| 0.8069 | 4.59 | 16000 | inf | 0.1877 |
| 0.8064 | 4.87 | 17000 | inf | 0.1955 |
| 0.801 | 5.16 | 18000 | inf | 0.1891 |
| 0.8022 | 5.45 | 19000 | inf | 0.1895 |
| 0.792 | 5.73 | 20000 | inf | 0.1854 |
It achieves the best result on the validation set on STEP 13000:
- Wer: 0.1834
Some problem occurs when calculating the validation loss.
Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.3.dev0
- Tokenizers 0.11.0
Evaluation Commands
- To evaluate on
mozilla-foundation/common_voice_8with splittest
python eval.py --model_id Plim/xls-r-1b-cv_8-fr --dataset mozilla-foundation/common_voice_8_0 --config fr --split test
- To evaluate on
speech-recognition-community-v2/dev_data
python eval.py --model_id Plim/xls-r-1b-cv_8-fr --dataset speech-recognition-community-v2/dev_data --config fr --split validation --chunk_length_s 5.0 --stride_length_s 1.0
Evaluation Results
Without LM:
| Dataset | WER | CER |
|---|---|---|
| TEST CV | 18.33 | 5.60 |
| DEV audio | 31.33 | 13.20 |
| TEST audio | / | / |
With LM:
| Dataset | WER | CER |
|---|---|---|
| TEST CV | 15.40 | 5.36 |
| DEV audio | 25.05 | 12.45 |
| TEST audio | / | / |
- Task
- Automatic Speech Recognition
- Library
- transformers
- Type
- wav2vec2
- License
- apache-2.0
- Languages
- fr
- Created
- Mar 2, 2022
- Updated
- Mar 24, 2022